The molecular composition of these alloy materials is an important prior knowledge for images generation task, so embedding molecular composition features into the generation model is needed. Let \(E=\lbrace Al,Mg,Si,Cu,Fe,O...\rbrace \)  be the set of all the elements appeared. Let \(m^i \in \mathbb {R}^{|E|}\)  be the element percentage representation vector of the alloy material \(i\) . Inspired by the word2vec method [1]} for word embedding, a method for molecular composition of alloy materials feature extracting called moleculars2vec is proposed. \(Ph_m = fc(m)\)  is used to learn the thermodynamic properties \(Ph_m\)  from the alloy material by the element representation vector \(m\)  :
\( Ph_m = fc(m) = Relu(W_m fc^{\prime }(m) ) \) 
